Detection of cardiac conditions from reduced lead set ecg

ABSTRACT

Embodiments of the present disclosure utilize DNNs for ECG interpretation, where original ECG waveforms are directly ingested by the DNNs using paired interpretation labels for training, without the need for explicatory feature extraction or rule-based criteria.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Application No.63/328,670, filed Apr. 7, 2022 and entitled “SINGLE ALGORITHM DETECTIONOF CARDIAC CONDITIONS FROM REDUCED LEAD SET ECG,” the disclosure ofwhich is hereby incorporated by reference.

TECHNICAL FIELD

Aspects of the present disclosure relate to diagnosis of cardiacconditions based on an electrocardiogram taken with a reduced lead set,and in particular to algorithmically diagnosing cardiac conditions usinga reduced lead set and a single algorithm.

BACKGROUND

Cardiovascular diseases are the leading cause of death in the world. In2008, 30% of all global death can be attributed to cardiovasculardiseases. It is also estimated that by 2030, over 23 million people willdie from cardiovascular diseases annually. Cardiovascular diseases areprevalent across populations of first and third world countries alike,and affect people regardless of socioeconomic status.

Arrhythmia is a cardiac condition in which the electrical activity ofthe heart is irregular or is faster (tachycardia) or slower(bradycardia) than normal. Although many arrhythmias are notlife-threatening, some can cause cardiac arrest and even sudden cardiacdeath. Indeed, cardiac arrhythmias are one of the most common causes ofdeath when travelling to a hospital. Atrial fibrillation (A-fib) is themost common cardiac arrhythmia. In A-fib, electrical conduction throughthe ventricles of heart is irregular and disorganized. While A-fib maycause no symptoms, it is often associated with palpitations, shortnessof breath, fainting, chest pain or congestive heart failure and alsoincreases the risk of stroke. A-fib is usually diagnosed by taking anelectrocardiogram (ECG) of a subject. To treat A-fib, a patient may takemedications to slow heart rate or modify the rhythm of the heart.Patients may also take anticoagulants to prevent stroke or may evenundergo surgical intervention including cardiac ablation to treat A-fib.In another example, an ECG may provide decision support for AcuteCoronary Syndromes (ACS) or other cardiac conditions by interpretingvarious rhythm and morphology conditions, including MyocardialInfarction (MI) and Ischemia.

Often, a patient with A-fib (or other type of arrhythmia) is monitoredfor extended periods of time to manage the disease. For example, apatient may be provided with a Holter monitor or other ambulatoryelectrocardiography device to continuously monitor the electricalactivity of the cardiovascular system for e.g., at least 24 hours. Suchmonitoring can be critical in detecting conditions such as acutecoronary syndrome (ACS), among others.

The American Heart Association and the European Society of Cardiologyrecommends that a 12-lead ECG should be acquired as early as possiblefor patients with possible ACS or other cardiac conditions when symptomspresent. Prehospital ECG has been found to significantly reducetime-to-treatment and shows better survival rates. The time-to-first-ECGis so vital that it is a quality and performance metric monitored byseveral regulatory bodies. According to the national health statisticsfor 2015, over 7 million people visited the emergency department (ED) inthe United States (U.S.) with the primary complaint of chest pain orrelated symptoms of cardiac conditions. In the US, ED visits areincreasing at a rate of or 3.2% annually and outside the U.S. ED visitsare increasing at 3% to 7%, annually.

BRIEF DESCRIPTION OF THE DRAWINGS

The described embodiments and the advantages thereof may best beunderstood by reference to the following description taken inconjunction with the accompanying drawings. These drawings in no waylimit any changes in form and detail that may be made to the describedembodiments by one skilled in the art without departing from the spiritand scope of the described embodiments.

FIG. 1A is a diagram that illustrates an example system, in accordancewith some embodiments of the present disclosure.

FIG. 1B is a block diagram that illustrates an example system, inaccordance with some embodiments of the present disclosure.

FIG. 2 is a diagram illustrating various leads that an ECG can becomprised of, in accordance with some embodiments of the presentdisclosure.

FIG. 3 is a block diagram that illustrates an example system, inaccordance with some embodiments of the present disclosure.

FIG. 4 is a block diagram illustrating the structure of machine learning(ML) model for performing reduced lead ECG interpretation, in accordancewith some embodiments of the present disclosure.

FIG. 5A is a block diagram illustrating the basic structure of a deepneural network (DNN), in accordance with some embodiments of the presentdisclosure.

FIG. 5B is a block diagram that illustrates an example convolutionalneural network (CNN), in accordance with some embodiments of the presentdisclosure.

FIG. 5C is a block diagram that illustrates an example training processfor a CNN, in accordance with some embodiments of the presentdisclosure.

FIG. 6 is a block diagram illustrating an example ECG rhythmclassification model, in accordance with some embodiments of the presentdisclosure.

FIG. 7A is a block diagram illustrating an example ECG morphologyclassification model, in accordance with some embodiments of the presentdisclosure.

FIGS. 7B and 7C are block diagrams illustrating an ECG signal and amedian beat of the ECG signal respectively, in accordance with someembodiments of the present disclosure.

FIG. 8 is a block diagram illustrating an example global ECG featuremodel, in accordance with some embodiments of the present disclosure.

FIG. 9 illustrates example conditions for validating classifications ofthe ECG rhythm classification model of FIG. 6 , in accordance with someembodiments of the present disclosure.

FIG. 10 is a flow diagram of a method for training and implementing areduced lead ECG interpretation model, in accordance with someembodiments of the present disclosure.

FIG. 11 is a block diagram of an example computing device that mayperform one or more of the operations described herein, in accordancewith some embodiments of the present disclosure.

DETAILED DESCRIPTION

In the following detailed description of embodiments of the presentdisclosure, numerous specific details are set forth in order to providea more thorough understanding of the disclosure. However, it will beapparent to one of ordinary skill in the art that the concepts withinthe disclosure can be practiced without these specific details. In otherinstances, well-known features have not been described in detail toavoid unnecessarily complicating the description.

An electrocardiogram (ECG) provides a number of ECG waveforms thatrepresent the electrical activity of a person's heart. An ECG monitoringdevice may comprise a set of electrodes for recording these ECGwaveforms (also referred to herein as “taking an ECG”) of the patient'sheart. The set of electrodes may be placed on the skin of the patient inmultiple locations and the electrical signal (ECG waveform) recordedbetween each electrode pair in the set of electrodes may be referred toas a lead. Varying numbers of leads can be used to take an ECG, anddifferent numbers and combinations of electrodes can be used to form thevarious leads. Example numbers of leads used for taking ECGs are 1, 2,3, 6, and 12 leads.

The ECG waveforms (each one corresponding to a lead of the ECG) recordedby the ECG monitoring device may comprise data corresponding to theelectrical activity of the person's heart. A typical heartbeat mayinclude several variations of electrical potential, which may beclassified into waves and complexes, including a P wave, a QRS complex,a T wave, and a U wave among others, as is known in the art. Stateddifferently, each ECG waveform may include a P wave, a QRS complex, a Twave, and a U wave among others, as is known in the art. The shape andduration of these waves may be related to various characteristics of theperson's heart such as the size of the person's atrium (e.g., indicatingatrial enlargement) and can be a first source of heartbeatcharacteristics unique to a person. The ECG waveforms may be analyzed(typically after standard filtering and “cleaning” of the signals) forvarious indicators that are useful in detecting cardiac events orstatus, such as cardiac arrhythmia detection and characterization. Suchindicators may include ECG waveform amplitude and morphology (e.g., QRScomplex amplitude and morphology), R wave-ST segment and T waveamplitude analysis, and heart rate variability (HRV), for example.

As noted above, ECG waveforms are generated from measuring multipleleads (each lead formed by a different electrode pair), and the ECGwaveform obtained from each different electrode pair/lead may bedifferent/unique (e.g., may have different morphologies/amplitudes).This is because although the various leads may analyze the sameelectrical events, each one may do so from a different angle. FIG. 1Aillustrates a view 105 of an ECG waveform detected by each of 3 leads(I, II, and III) when a 3-lead ECG is taken as well as an exploded view110 of the ECG waveform measured by lead III illustrating the QRScomplex. As shown, the amplitudes and morphologies of the ECG waveformtaken from leads I-III are all different, with the ECG waveform measuredby lead III having the largest amplitude and the ECG waveform measuredby lead I having the smallest amplitude.

There are different “standard” configurations for electrode placementthat can be used to place electrodes on the patient. For example, anelectrode placed on the right arm can be referred to as RA. Theelectrode placed on the left arm can be referred to as LA. The RA and LAelectrodes may be placed at the same location on the left and rightarms, preferably near the wrist in some embodiments. The leg electrodescan be referred to as RL for the right leg and LL for the left leg. TheRL and LL electrodes may be placed on the same location for the left andright legs, preferably near the ankle in some embodiments. Lead I istypically the voltage between the left arm (LA) and right arm (RA), e.g.I=LA−RA. Lead II is typically the voltage between the left leg (LL) andright arm (RA), e.g. II=LL−RA. Lead III is the typically voltage betweenthe left leg (LL) and left arm (LA), e.g. III=LL−LA. Augmented limbleads can also be determined from RA, RL, LL, and LA. The augmentedvector right (aVR) lead is equal to RA−(LA+LL)/2 or −(I+II)/2. Theaugmented vector left (aVL) lead is equal to LA−(RA+LL)/2 or I−II/2. Theaugmented vector foot (aVF) lead is equal to LL−(RA+LA)/2 or II−I/2.

It should be noted that a set of two or more leads may be transformed togenerate a full, 12-lead ECG. Such transformation may be performed usinga machine learning model (e.g., a neural network, deep-learningtechniques, etc.). The machine learning model may be trained using12-lead ECG data corresponding to a population of individuals. The data,before being input into the machine learning model, may be pre-processedto filter the data in a manner suitable for the application. Forexample, data may be categorized according to height, gender, weight,nationality, etc. before being used to train one or more machinelearning models, such that the resulting one or models are finely-tunedthe specific types of individuals. In a further embodiment, the machinelearning model may be further trained based on a user's own ECG data, tofine-tune and personalize the model even further to decrease anyresidual synthesis error.

As discussed herein, a 12-lead ECG should be acquired as early aspossible for patients with possible cardiac conditions when symptomspresent as prehospital ECG has been found to significantly reducetime-to-treatment and shows better survival rates. In addition, currentambulatory ECG devices such as Holter monitors, are typically bulky anddifficult for subjects to administer without the aid of a medicalprofessional. For example, the use of Holter monitors requires a patientto wear a bulky device on their chest and precisely place a plurality ofelectrode leads on precise locations on their chest. These requirementscan impede the activities of the subject, including their naturalmovement such as bathing and showering. Once an ECG is taken by suchdevices, the ECG is sent to the subject's physician who then analyzesthe ECG waveforms and provides a diagnosis and other recommendations.Currently, this process often must be performed through hospitaladministrators and health management organizations and many patients donot receive feedback in an expedient manner.

A 12 lead ECG may be administered in a hospital or medical clinicsetting in order to detect cardiac conditions, however it would bedesirable to have a simpler reduced lead system that could be used athome, or doctor's office, or emergent care, or prehospital in order todetect cardiac conditions. Certain reduced lead (e.g., 6 lead) devicesexist that can construct all 12 leads mathematically based on a reducedlead set by either linear or non-linear transformations in order toprovide a set of all 12 leads (some are now “synthesized leads”). Thissynthesized complete 12 lead data can be analyzed by a doctor or a full12 lead algorithm. Based on existing techniques to reconstruct a full 12lead set, a natural workflow for an automated system would be thefollowing: 1) use a lead reconstruction algorithm to create a 12-leadset from a reduced lead set (say 4 leads), and then 2) use a pathologyclassification algorithm to take as input the reconstructed 12 leads andas output provide determinations (classifications) of normal versusvarious pathologies.

However, there is an intrinsic trade-off with such reduced lead devicesas they provide less resolution/ability to detect and discriminate thefull range of heart pathologies using the standard 12-lead ECG criteriarecommended by cardiology society (ACC/AHA). It would be desirable tohave the discriminating ability of a 12 lead system with the simplicityof a reduced set of leads that could be used outside of a hospital (athome, in emergency response, in doctor's offices, etc.) but without thetrade-off of sacrificing resolution to discriminate.

A deep neural network (DNN) is a type of model that consists of multiplelayers of interconnected nodes, each building upon the previous layer torefine and optimize the prediction or categorization. This progressionof computations through the network is called forward propagation. Theinput and output layers of a deep neural network are called visiblelayers. The input layer is where the deep learning model ingests thedata for processing, and the output layer is where the final predictionor classification is made. A DNN is known to have excellent performancein highly non-linear and highly composite systems (especially whentrying to “subsume” multiple algorithmics steps into a single step). ADNN can essentially subsume all algorithm(s) used to make diagnosesbased on ECG data into a single mathematical process using a techniquecalled “end-to-end” learning. By utilizing this end-to-end training ofwhat was multiple tasks/ML, models, the resulting DNN can provide highaccuracy results, which can often exceed the performance provided by theuse of multiple dedicated algorithms.

FIG. 1B shows a system 100 for cardiac disease management. The system100 may be prescribed for use by a first user e.g., by the first user'sphysician. Alternatively, system 100 may be used without input from aphysician or other third party. The system 100 may comprise a localcomputing device 101 of the first user. The local computing device 101may be loaded with a user interface, dashboard, or other sub-system ofthe cardiac disease management system 100. For example, the localcomputing device 101 may be loaded with a mobile software application(“mobile app”) 101A for interfacing with the system 100. The mobile app101A may be configured to interface with one or more biometric sensors(e.g., ECG monitor 103) and may comprise software and a user interfacefor managing biometric data collected by the local computing device 101from one or more biometric sensors. The local computing device 101 maycomprise any appropriate computing device, such as a tablet computer, asmartphone, a server computer, a desktop computer, a laptop computer, ora body-worn computing device (e.g., a smart watch or other wearable),for example. In some embodiments, the local computing device 101 maycomprise a single computing device or may include multipleinterconnected computing devices (e.g., multiple servers configured in acluster).

The local computing device 101 may be coupled to one or more biometricsensors. For example, the local computing device 101 may be coupled toan ECG monitor 103 which may comprise a set of electrodes for recordingECG (electrocardiogram) data (also referred to herein as “taking anECG”) of the first user's heart. The ECG data can be recorded or takenusing the set of electrodes which are placed on the skin of the firstuser in multiple locations. The electrical signals recorded betweenelectrode pairs may be referred to as leads and Varying numbers of leadscan be used to record the ECG data, and different numbers andcombinations of electrodes (placed on the user's arms, legs, and chest)can be used to form the various leads. Example numbers of leads used fortaking ECGs are 1, 2, 6, and 12 leads. The electrode placed on the rightarm may be referred to as RA. The electrode placed on the left arm maybe referred to as LA. The RA and LA electrodes may be placed at the samelocation on the left and right arms, e.g., near the wrist. The legelectrodes may be referred to as RL for the right leg and LL for theleft leg. The RL and LL electrodes may be placed on the same locationfor the left and right legs, e.g., near the ankle.

FIG. 2 illustrates a single dipole heart model 115 with a 12-lead setcomprising the I, II, III, aVR, aVL, aVF, V1, V2, V3, V4, V5, and V6leads, all represented on a hexaxial system. The heart model 115 assumesa homogeneous cardiac field in all directions that only changesmagnitude and direction with the cycle time. As illustrated in FIG. 2 ,there are 2 orthogonal planes, the frontal plane and the horizontalplane. Inside each plane, there are several leads to cover the wholeplane. In the frontal plane, there are 2 independent leads I and II, and4 other derived leads III, aVR, aVL, and aVF, each 30 degrees apart. Thereason the frontal plane has 2 independent leads is that they arefar-field leads, each of which can cover a wider perspective but provideless detail, like a wide-angle camera lens. In the horizontal plane,there are normally 6 independent leads which are all closer to the heartthan limb leads and may be referred to as near-field leads. Followingthe same analogy of a camera, the near-field leads may behave like azoom-lens that covers less perspective, but with more accuracy towardslocal activity like ischemia and infarction. The two orthogonal planesare related by using a synthetic reference point formed by Leads I & II,called the Wilson-Central-Terminal (WCT). It is defined as RA+LA+RL/3but given that both Lead I and II are recorded with reference to the RAso that the voltage of the RA can be considered zero, the WCT (VW) canbe calculated using the RA as the reference for both Leads I & II (thus,assuming it to have zero potential) as: Lead I+Lead II/3.

Referring back to FIG. 1B, in some embodiments, the ECG monitor 103 maycomprise a handheld ECG monitor (such as the KardiaMobile® orKardiaMobile® 6L device from AliveCor® Inc., for example) comprising asmaller number of electrodes (e.g., two, three, four, or any number ofelectrodes less than twelve). In these embodiments, the electrodes canbe used to measure a subset of the leads illustrated in FIG. 2 , such aslead I (e.g., the voltage between the left arm and right arm)contemporaneously with lead II (e.g., the voltage between the left legand right arm), and lead I contemporaneously with lead V2 or another oneof the chest leads such as V5. It should be noted that any othercombination of leads is possible. If desired, additional leads can thenbe algorithmically derived (e.g., by the ECG monitor 103 itself or thelocal computing device 101) from the determined subset of leads. Forexample, augmented limb leads can also be determined from the valuesmeasured by the LA, RA, LL, and RL electrodes. The augmented vectorright (aVR) may be equal to RA−(LA+LL)/2 or −(I+II)/2. The augmentedvector left (aVL) may be equal to LA−(RA+LL)/2 or I−II/2. The augmentedvector foot (aVF) may be equal to LL−(RA+LA)/2 or II−I/2. In someembodiments, the ECG monitor 103 itself or the local computing device101 may utilize a machine learning (ML) model to derive the full 12 leadset from a measured subset of leads. In some embodiments, the ECGmonitor 103 may be in the form of a smartphone, or a wearable devicesuch as a smart watch. In some embodiments, the ECG monitor 103 may be ahandheld sensor coupled to the local computing device 101 with anintermediate protective case/adapter.

The ECG data recorded by the ECG monitor 103 may comprise the electricalactivity of the first user's heart, for example. A typical heartbeat mayinclude several variations of electrical potential, which may beclassified into waves and complexes, including a P wave, a QRS complex,a T wave, and sometimes U wave as known in the art. The shape andduration of the P wave can be related to the size of the user's atrium(e.g., indicating atrial enlargement) and can be a first source ofheartbeat characteristics unique to a user.

The QRS complex can correspond to the depolarization of the heartventricles, and can be separated into three distinct waves—a Q wave, a Rwave and a S wave. Because the ventricles contain more muscle mass thanthe atria, the QRS complex is larger than the P wave. Also, theHis/Purkinje system of the heart, which can increase the conductionvelocity to coordinate the depolarization of the ventricles, can causethe QRS complex to look “spiked” rather than rounded. The duration ofthe QRS complex of a healthy heart can be in the range of 60 to 100milliseconds (ms), but can vary due to abnormalities of conduction. Theduration of the QRS complex can serve as another source of heartbeatcharacteristics unique to a user.

The duration, amplitude, and morphology of each of the Q, R and S wavescan vary in different individuals, and in particular can varysignificantly for users having cardiac diseases or cardiacirregularities. For example, a Q wave that is greater than ⅓ of theheight of the R wave, or greater than 40 ms (milliseconds) in durationcan be indicative of a myocardial infarction and provide a uniquecharacteristic of the user's heart. Similarly, other healthy ratios of Qand R waves can be used to distinguish different users' heartbeats.

The electrical activity of the user US's heart can also include one ormore characteristic durations or intervals that can be used todistinguish different users. For example, the electrical activity of theheart may include PR intervals and ST segments as known in the art. A PRinterval can be measured from the beginning of P wave to the beginningof a QRS complex. A PR interval can typically last 120 to 200 ms. A PRinterval having a different duration can indicate one or more defects inthe heart, such as a first degree heart block (e.g., a PR intervallasting more than 200 ms), a pre-excitation syndrome via an accessorypathway that leads to early activation of the ventricles (e.g., a PRinterval lasting less than 120 ms), or another type of heart block(e.g., a PR interval that is variable). An ST segment can be measuredfrom a QRS complex to a T wave, for example starting at the junctionbetween the QRS complex and the ST segment and ending at the beginningof the T wave. An ST segment can typically last from 80 to 120 ms, andnormally has a slight upward concavity. The combination of the length ofST segment, and the concavity or elevation of ST segment can also beused to generate characteristic information unique to each user'sheartbeat.

The ECG monitor 103 may be used by the user to measure their ECG datafor analysis as discussed in further detail herein. The ECG monitor 103may also transmit the measured ECG data and/or the results of thediagnosis analysis (as discussed in further detail herein) to the localcomputing device 101 by any appropriate wired or wireless connectionsuch as e.g., a Wi-Fi connection, a Bluetooth® connection, a near-fieldcommunication (NFC) connection, an ultrasound signal transmissionconnection, etc.

The ECG data may be continually recorded by the user at regularintervals. For example, the interval may be once a day, once a week,once a month, or some other predetermined interval. The ECG data may berecorded at the same or different times of days, under similar ordifferent circumstances, as described herein. The ECG data may also berecorded at the same or different times of the interval (e.g., the ECGdata may be captured asynchronously). Alternatively, or additionally,the ECG data can be recorded on demand by the user at various discretetimes, such as when the user feels chest pains or experiences otherunusual or abnormal feelings, or in response to an instruction to do sofrom e.g., the user's physician. In another embodiment, ECG data may becontinuously recorded over a period of time.

Each ECG data recording may be time stamped and may be annotated withadditional data by the user or health care provider to describe usercharacteristics. For example, the local computing device 101 (e.g., themobile app 101A thereof) may include a user interface for data entrythat allows the user to enter their user characteristics. Examples ofuser characteristics may include age, sex, race, ethnicity, relevantmedical history, location, diet (e.g., food/drink habits),medication/drug consumption, exercise patterns, sleep/rest patterns,feelings of stress, anxiety, pain or other unusual or abnormal feelings,activities performed before, during, or after the data recording, or anyother user specific circumstance or factor that may affect the user'sECG data. The local computing device 101 may append the usercharacteristics to the ECG data and store the ECG data and usercharacteristics for further analysis (e.g., ECG interpretation).Alternatively, the local computing device 101 may transmit the ECG dataand user characteristics (collectively referred to as user data) to thecloud storage system 113. Because the user data is time stamped ortagged, the ECG data can be matched or correlated with an activity orcircumstance of interest. As described in further detail herein, thisalso allows for comparison of the ECG data before, after and during theactivity or circumstance of interest so that the effect on the ECG datacan be determined and accounted for during further analysis.

When the user data is transmitted by the local computing device 101 tothe cloud storage system 113 for storage and analysis, such transmissioncan be real-time, at regular intervals such as hourly, daily, weeklyand/or any interval in between, or can be on demand. The local computingdevice 101 and the cloud storage system 113 may be coupled to each other(e.g., may be operatively coupled, communicatively coupled, maycommunicate data/messages with each other) via network 140. Network 140may be a public network (e.g., the internet), a private network (e.g., alocal area network (LAN) or wide area network (WAN)), or a combinationthereof. In one embodiment, network 140 may include a wired or awireless infrastructure, which may be provided by one or more wirelesscommunications systems, such as a Wi-Fi hotspot connected with thenetwork 140 and/or a wireless carrier system that can be implementedusing various data processing equipment, communication towers (e.g.,cell towers), etc. The network 140 may carry communications (e.g., data,message, packets, frames, etc.) between the local computing device 101and the cloud storage system 113.

The user data accumulated over time for a particular user may form atime series health record for that particular user. The cloud storagesystem 113 may comprise any suitable type of computing device or machinethat has a programmable processor including, for example, a servercomputer, a desktop computer, laptop computer, tablet computer,smartphone, etc. In some embodiments, the cloud storage system 113 maycomprise a single computing device or may include multipleinterconnected computing devices (e.g., multiple servers configured in acloud storage cluster).

FIG. 3 illustrates a hardware block diagram of the computing device 101.The computing device 101 may include hardware such as processing device115 (e.g., processors, central processing units (CPUs)), memory 120(e.g., random access memory (RAM)), storage devices (e.g., hard-diskdrive (HDD), solid-state drive (SSD), etc.), and other hardware devices(e.g., sound card, video card, etc.). In some embodiments, memory 120may be a persistent storage that is capable of storing data. Apersistent storage may be a local storage unit or a remote storage unit.Persistent storage may be a magnetic storage unit, optical storage unit,solid state storage unit, electronic storage units (main memory), orsimilar storage unit. Persistent storage may also be a monolithic/singledevice or a distributed set of devices. Memory 120 may be configured forlong-term storage of data and may retain data between power on/offcycles of the computing device 101. The memory 120 may store the userdata accumulated over time for the user as well as a multitude of otherusers. The memory 120 may further include a reduced lead ECG analysismodel 120A that is configured to analyze reduced lead ECG data of theuser that is measured each time an ECG is performed to determine whetherthe user is experiencing a cardiac condition as discussed in furtherdetail herein.

As discussed herein, mobile ECG monitors attempt to simplify the processof taking an ECG by using a reduced set of leads e.g., any number ofleads less than 12. However, such implementations must intrinsicallysacrifice resolution or discriminating ability when detecting the fullrange of heart pathologies for the convenience of a reduced lead set.Prior work has tried to construct all 12 leads mathematically based on areduced lead set by either linear or non-linear transformations in orderto provide a set of all 12 leads (some are now “synthesized leads”) andthis synthesized complete 12 lead data can be analyzed by a doctor or afull 12 lead algorithm. Based on this prior work to reconstruct a full12 lead set, a natural workflow for an ML model implemented by themobile ECG device would also involve multiple layers including forexample: 1) use a lead reconstruction algorithm to create a 12-lead setfrom a reduced lead set (say 4 leads), and 2) use a pathologyclassification algorithm to take as input the reconstructed 12 leads andprovide as output provide determinations (classifications) of normalversus various pathologies. However, the application of reduced lead12-lead sets and synthesized leads can be limited because ECG criteriathat is developed with standard 12-lead ECGs is applied for abnormalcase detection and interpretation. There is no specific criteria thathas been developed or recommended by ACC/AHA for the reduced lead sets.This results in gaps in accuracy for such synthesized leads, especiallyin localized STEMI situations.

In addition, solving the problem of providing accurate readings ofreduced lead ECGs requires an ML model architecture consisting ofdifferent layers (e.g., preprocessing, feature extraction, optimization,prediction, decision making) including lead reconstruction and pathologyclassification that each process input data in a pipe-lined fashion.However, such a multi-layer approach may provide less than ideal resultsdue to the complexity of the model. For example, such a multi-layerarchitecture requires that each layer be optimized separately underdifferent criteria.

A deep neural network (DNN) is a type of model that consists of multiplelayers of interconnected nodes, each building upon the previous layer torefine and optimize the prediction or categorization. This progressionof computations through the network is called forward propagation. Theinput and output layers of a deep neural network are called visiblelayers. The input layer is where the deep learning model ingests thedata for processing, and the output layer is where the final predictionor classification is made. For example, we may have a set of photos ofdifferent pets, and may wish to categorize by “cat”, “dog”, “hamster”,etc. Deep learning algorithms can determine which features (e.g., ears)are most important to distinguish each animal from another. In machinelearning, this hierarchy of features is established manually by a humanexpert. Then, through the processes of gradient descent andbackpropagation, the DNN adjusts and fits itself for accuracy, allowingit to make predictions about a new photo of an animal with increasedprecision. Back-propagation involves fine-tuning the weights of a neuralnet based on the error rate (i.e., loss) obtained in the previous epoch(i.e. iteration). Proper tuning of the weights ensures lower errorrates, making the model reliable by increasing its generalization.

DNNs can also be trained using end-to-end learning, which takesadvantage of a DNNs structure, which is composed of several layers, tosolve complex problems. For ECG processing, the input end is an ECGwaveform, and the output end are the classification results. Each DNNlayer (or group of layers) can specialize to perform intermediate tasksnecessary for such problems. For example, the convolution layers (CNN)of the DNN model can extract ECG features automatically. But thosefeatures are mostly distributed in a vector of the intermediate outputlayer, called flatten layer, at the end of CNN blocks. The elements inthe flatten layer are also called embedding variables, which can be usedeither for classification of the rhythm and morphology interpretations,or regression to predict ECG measurements like PR interval, QRSduration, and QT interval End-to-end learning refers to training apossibly complex learning system represented by a single model(specifically a DNN) that represents the complete target system,bypassing the intermediate layers usually present in traditionalpipeline designs. Stated differently, when two or more ML models areserving as components to a larger architecture, a DNN may be trained inan end-to-end manner which involves simultaneously training allcomponents (i.e., training it as a single network).

Embodiments of the present disclosure utilize DNNs for ECGinterpretation, where original ECG waveforms are directly ingested bythe DNNs using paired interpretation labels for training, without theneed for explicatory feature extraction or rule-based criteria. Asdiscussed in further detail herein, the embodiments of the presentdisclosure differ from conventional expert system-based criteria sinceDNN models play a significant role in the overall ECG classification andECG interpretation process.

FIG. 4A illustrates the reduced lead ECG analysis model 120A(hereinafter referred to as interpretation model 120A). Theinterpretation model 120A includes a data conditioning stage 130, amodel analysis stage 140, and a final determination stage 150. The dataconditioning stage 130 may include filtering and signal processingfunctions, as well as a down sampling module 131, a lead conversionmodule 132, and a median beats generation module 133. For example, theinterpretation model 120A may receive as input (e.g., from ECG monitor103), a first 4-lead ECG signal including leads, [I, II, v2, v4] and asecond 4-lead ECG signal including leads [I, II, v1, v4], where thesignal from each lead is 10 seconds. Although illustrated as using 10second samples of leads [I, II, v1/v2, v4], this is for example purposesand is not a limitation. Indeed, any appropriate set of input leads maybe used and each lead's signal may have any appropriate duration. Thedown sampling module 131 may down-sample each input 4-lead ECG signal to150 Hz, while the lead conversion module 132 may determine another 4limb leads, [III, aVR, aVL, aVF] using conventional lead reconstructionalgorithms. The lead conversion module 132 may add the algorithmicallyreconstructed leads to each of the 4-lead ECG signals resulting in two 8lead ECG signals. A first 8 lead ECG signal may include leads [I, II,III, v2, v4, aVR, aVL, aVF] while the second 8 lead ECG signal mayinclude leads [I, II, III, v1, v4, aVR, aVL, aVF].

Training ML models to classify ECG signals requires determining theunderlying characteristics of the ECG signals. The model analysis stage140 may include ML models to identify the underlying characteristics ofthe input ECG signals including ECG rhythm patterns and ECG waveformmorphology/shape patterns. Each of the ML models in the model analysisstage 140 may be any appropriate deep neural network (DNN) model such asa convolutional neural network (CNN) or any other appropriate DNN.Determining ECG rhythm patterns requires capturing the sequentialcharacteristics of the ECG rhythm patterns, which in turn requiresdetecting those characteristics effectively. Thus, the model analysisstage 140 may include for each of the 8 lead ECG signals, an ECG rhythmclassification model 141. Stated differently, each 8 lead ECG signal mayserve as an input to a corresponding ECG rhythm classification model141. An adequate time window for the input data (in this case, a 10second window for the two 8 lead ECG signals) should be used to allowfor the sequential characteristics of rhythm patterns to be detectedwithout further features detection.

Besides rhythm patterns, waveform morphology/shape patterns are anothermajor ECG characteristic, and are usually detected with ECG amplitudeand duration/intervals. For waveform morphology, different types ofrepresentative beats of a data series (e.g., median beats, average beatsof multiple ECG cycles) can simplify a waveform morphology detectionmodel and speed up the training, while also improving the classificationaccuracy. Thus, a median beats generation module 133 may determine themedian beats of the first and second 8 lead ECG signals resulting infirst and second median beat 8 lead signals respectively. The modelanalysis stage 140 may include for each of the median beat 8 leadsignals, an ECG morphology model 142. Stated differently, each medianbeat 8 lead signal may serve as an input to a corresponding ECGmorphology ML model 142. The model analysis stage 140 may furtherinclude a global ECG measurements model 143 for detecting global ECGparameters including e.g., PR interval, QRS duration, and QT interval.The embodiments of the present disclosure are described with respect toonly the first 8 lead ECG signal and the corresponding ECG rhythmclassification model 141 and ECG morphology ML model 142, however thisis for ease of description and each 8 lead ECG signal may have its ownDNN models that may form corresponding classifications as described infurther detail herein.

The final determination stage 150 may include final ECG rhythm logic 151and final ECG morphology logic 152, each of which may include somesimple logic to validate a classification by a corresponding DNN. Thefinal analysis combines all DNN output and ECG measurements like PR,QRSD, QT interval, ST levels to refine the classifications. The outputsof the final determination stage 150 may be probabilities predicting thelikelihood of various (often times hard-to-discriminate) heartpathologies (or an indication that the user's cardiac health is normal).

FIG. 5A illustrates the general structure 500 of each model 141, 142 and143 used in the model analysis stage 140. Each model may include 4 majorcomponents. The first component may be a lead formation layer 505 whichreformats the leads of the 8 lead ECG signal (or the median beat 8 leadsignal), to a format needed for training the model. The second componentmay be a set of convolution layers 510, which may include a number(e.g., 5-8) of CNN blocks with different numbers of filters for eachblock. The third component may be a set of dense layers 515, whichreshape the multi-filter output of the final convolution layer to aflattened layer and pass the results to the next dense layers with fullconnections, meaning each unit of the previous layer is connected to allunits of the next layer. The fourth component may be a classificationlayer 520, which generates a probability for each determination.

FIG. 5B illustrates an example of a DNN 400 that could be used inconjunction with some embodiments of the present disclosure. The DNN 400may comprise a trained CNN model that takes input data 402 (e.g., userdata) into convolutional layers (aka hidden layers) 403, applies aseries of trained weights or filters 404 to the input data 406 in eachof the convolutional layers 403. The output of the first convolutionallayer is an activation map (not shown), which is the input to the secondconvolution layer, to which a trained weight or filter (not shown) isapplied, where the output of the subsequent convolutional layers resultsin activation maps that represent more and more complex features of theinput data to the first layer. After each convolutional layer anon-linear layer (not shown) is applied to introduce non-linearity intothe problem, which nonlinear layers may include an activation functionsuch as tan h, sigmoid or ReLU. In some cases, a pooling layer (notshown) may be applied after the nonlinear layers, also referred to as adownsampling layer, which takes a filter and stride of the same lengthand applies it to the input, and outputs the maximum number in everysub-region the filter convolves around. Other options for pooling areaverage pooling and L2-norm pooling. The pooling layer reduces thespatial dimension of the input volume reducing computational costs andto control over-fitting. The final layer(s) of the network is a fullyconnected layer, which takes the output of the last convolutional layerand outputs an n-dimensional output vector representing the quantity tobe predicted, e.g., probabilities of LVH diagnosis and (if applicable)severity of LVH. This may result in predictive output 406 (O*), e.g.,this user is likely suffering from stage 2 LVH. The trained weights 404may be different for each of the convolutional layers 403, as will bedescribed more fully below.

FIG. 5C demonstrates the process of training the DNN 400 (without thedense layers and classification layer shown for simplicity). In FIG. 5Cconvolution layers 403 are shown as individual convolution layers 405,405′ to convolution layer (405)^(n) and the final nth layer is a fullyconnected layer. It will be appreciated that last layers may be morethan one fully connected layer. A training example 411 is input intoconvolution layers 403, a nonlinear activation function (not shown) andweights 410, 410′ through 410 ^(n) are applied to training example 411in series, where the output of any convolution layer is input to thenext layer, and so on until the final nth fully connected layer(405)^(n-1) produces output 414. The output or prediction 414 (*) iscompared against training example 411 (e.g., Wide QRS Tachycardia)resulting in difference 416 between output or prediction 414 andtraining example 411 (also shown as I_(known) in FIG. 6B). If thedifference or loss 416 is less than some preset loss (e.g., output orprediction 414 predicts the user is suffering from Wide QRS Tachycardiawith the requisite level of accuracy), the DNN model is converged andconsidered trained.

If the DNN 400 has not converged, using the technique ofbackpropagation, weights 410 and 410 ^(n-1) through 410 ^(n) are updatedin accordance with how close the prediction is to the known input. Theskilled artisan will appreciate that methods other than back propagationmay be used to adjust the weights. A second training example (e.g.,different user data) may be input and the process is repeated again withthe updated weights, which are then updated again and so on until thenth training example (e.g., nth user data) has been input. This isrepeated over and over with the same n-training examples until the DNNmodel is trained or converges on the correct outputs for the knowninputs. Once the DNN model is trained, weights 410 through 410′ arefixed and used in trained ECG rhythm classification model 141. Asdiscussed herein, there are different weights for each convolutionallayer 403 and for each of the fully connected layers. The trained ECGrhythm classification model 141 is then fed user data to determine orpredict that which it is trained to predict/identify (e.g., rhythmclassification), as described herein in further detail.

FIG. 6 illustrates a detailed structure of the ECG rhythm classificationmodel 141. The convolution layers 610 may utilize a residual blockstructure that utilizes combinations of convolution layers and skiplayers. A skip layer (also referred to as a skip connection or shortcutconnections) may enable a residual block in the deep learning models toaddress the problem of vanishing and exploding gradients, which canoccur when training very deep neural networks. This is done by addingthe input of a layer directly to the output of one or more subsequentlayers, creating a “shortcut” for the gradient to flow through. As aresult, different parts of the convolution layers 610 will be trained atdifferent rates for different training data points based on how theerror flows backward through the layers of the convolution layers 610.FIG. 6 illustrates a residual block 610A, where a skip layer is used toskip the training of the convolution layer 2 during learning of anidentity function X. ReLu may represent a nonlinear activation function.

In addition to the residual blocks, the ECG rhythm classification model141 may include 2 dense layers 615 and 1 classification layer 620. Insome embodiments, the ECG rhythm classification model 141 may furtherinclude a batch normalization layer and dropout layer for every residualblock and dense layer to improve its robustness or generalization onceit is trained (not every batch normalization and dropout layer shown inFIG. 6 ). The ECG rhythm classification model 141 is designed toclassify rhythm determinations, examples of which are shown in table 1(below). The classification layer 620 output may have both positive andnegative probabilities for each determination. In some embodiments, foreach of the 8 lead ECG signals (down-sampled to 150 Hz), 9.5 seconds outof the 10 seconds of data is used for the input and the remaining 0.5seconds of data may be used for augmentation with a time-shiftingtechnique (random data shifting). This random data shifting is one ofthe data augmentation methods used to improve the generalization of thetrained DNN models, which will result in the DNN models being lesssensitive to the start and the end times of the input ECG rhythm data.

TABLE 1 order Determinations 1 Normal Sinus Rhythm 2 Sinus rhythm 3Atrial fibrillation 4 Atrial flutter 5 1^(st) degree AV block 6 2^(nd)degree AV block (mobiz 1) 7 2^(nd) degree AV block (mobiz 2) 8 3^(rd)degree AV block (Complete AV block) 9 Wide QRS Tachycardia 10 SinusArrythmia 11 Marked Sinus Arrythmia 12 Paced rhythm 13 Markedbradycardia 14 Junctional rhythm 15 Ectopic Atrial rhythm 16 SuperVentricular Tachycardia 17 Undetermined rhythm 18 Sinus Tachycardia 19Bigeminy 20 PVCs

The ECG rhythm classification model 141 may be trained using anysufficiently large training data set. For example, the training data setmay include 1 million labeled ECGs, with another 250K labeled ECGs foruse as an independent test set. In the example of FIG. 6A, the batchsize is 512, the learning rate is 0.001, and the drop rate 0.05. A totalof 20 epochs are set. The final model is selected from the smallestvalidation error. A training weight matrix is used for balancing a largedifference in prevalence among all determinations. For the lowerprevalence determinations, a higher weight is used when the trainingerror is backpropagated.

FIG. 7A illustrates a detailed structure of the ECG morphology model142. The ECG morphology model 142 may capture morphology changes foreach lead and relative changes from lead to lead, referred to as changesin the spatial domain. The ECG morphology model 142 may have a similarmodel structure as the ECG rhythm classification model 141, but with asmaller lead formation layer and fewer residual block layers, since themedian beat 8 lead signal has a much shorter length than the 10 secondduration of the 8 lead ECG signal input to the ECG rhythm classificationmodel 141. In addition, all median beats are relatively aligned aroundtheir QRS onset. Another important design consideration for theMorphology model QRS region, is that the variations along the time axisare relatively smaller than rhythm signals.

To capture the spatial domain changes, the ECG morphology model 142 mayform an ECG imaging by laying out of all the leads in a 2-D matrix/imagepattern with the X-axis as time and the Y-axis as a lead order of [aVL,I, aVR, II, aVF, III, v2, v4], where the limb leads are reordered in aso-called Cabrera sequence. The ECG morphology model 142 may process the2-D ECG signal matrix with a 2-D convolutional filter like in imageclassification cases. This method is more effective than applyingmultiple 1-D convolutional filters to each lead separately, which hasbeen used by most other ECG training models. Since ECG is a projectionof heart's electrical depolarization and repolarization process to thetorso, a spatial 2-D signal matrix can capture this projection moreeffectively. Therefore, 2-D convolutional modes are applied to this ECGimage input.

As discussed herein, the median beats generation module 133 may generatethe median beat 8 lead signal based on the 8 lead ECG signal. Morespecifically, the median beats generation module 133 may form a QRSdetection function, perform beat detection and classification, performbeat alignment, and calculate the median and average beats from thedominant beat type. FIG. 7B shows an example 8 lead ECG signal and FIG.7C shows the median beat of the example 8 lead ECG signal. As can beseen, the ECG morphology features are captured in the median beat whilethe data length is much shorter.

The ECG morphology model 142 may be trained with the same training dataset as the ECG rhythm classification model 141 (which may include medianbeat data for each training ECG). In the example of FIG. 6A, the batchsize is 512, the learning rate is 0.001, and the drop rate 0.05. A totalof 20 epochs are set. The final model is selected from the smallestvalidation error. A morphology training weight matrix is also used forbalancing a large difference in prevalence among all determinations. Forthe lower prevalence determinations, a higher weight is used when thetraining error is backpropagated. The ECG morphology model 142 isdesigned to classify ECG morphology determinations, examples of whichare shown in table 2 (below). The classification layer of the ECGmorphology model 142 has both positive and negative probabilities foreach determination.

TABLE 2 order Determinations 1 Right bundle branch block (RBBB) 2 Leftbundle branch block (LBBB) 3 Other Intra-Ventricular block 4 LeftVentricle Hypertrophy (LVH) 5 Right Ventricle Hypertrophy (RVH) 6 RightAtrial Enlargement (RAE) 7 Right Atrial Enlargement (RAE) 8 Anterior MIold 9 Inferior MI old 10 Lateral MI old 11 Septal MI old 12 Anterior MIacute 13 Inferior MI acute 14 Lateral MI acute 15 Septal MI acute 16Anterior Ischemia 17 Inferior Ischemia 18 Lateral Ischemia 19 SeptalIschemia 20 Acute pericarditis 21 Early Repolarization 22 Prolonged QT23 Paced ECG 24 Wolff-Parkinson-White (WPW) 25 Normal ECG

The global ECG measurements model 143 may measure ECG global parametersincluding PR interval, QRS duration, QT interval, and heart rate. Theseparameters are referred to as ECG global measurements because they arenot lead-specific and are often estimated based on a combination ofmultiple leads. These parameters are often detected with complicatedsignal processing and feature detection algorithms based on domainknowledge. However, embodiments of the present disclosure estimate theseECG parameters using a deep learning model (the global ECG measurementsmodel 143) applied to the median beat 8 lead signal generated from the 8lead ECG signal. Because the ECG global parameters are closely relatedto ECG morphology, the global ECG measurements model 143 can be trainedto perform a regression of the median beat 8 lead ECG signal to detectthese parameters. Another aim of training the global ECG measurementsmodel 143 is to also be able to identify the absence of a valid P waveas in atrial fibrillation cases.

FIG. 8 illustrates a detailed structure of the global ECG measurementsmodel 143. The structure of the global ECG measurements model 143 islike the structure of the ECG morphology model 142 except the finalclassification layer of the global ECG measurements model 143 isreplaced by a regression layer 820. In addition, the global ECGmeasurements model 143 does not have the batch normalization layers ofthe ECG morphology model 142 since they are more useful forclassification than for regression. The global ECG measurements model143 may receive as input a median beat 4 lead signal based on the leadset [I, II, v2, v4]. More specifically, the median beats generationmodule 133 may determine the median beats for each lead of the lead set[I, II, v2, v4] as discussed hereinabove and provide this median beat 4lead signal to the global ECG measurements model 143. The global ECGmeasurements model 143 is trained to output three intervals: PRinterval, QRS duration, and QT interval by minimizing the absolute errorbetween labeled (physician-estimated) intervals from the training dataand the model-estimated intervals.

The global ECG measurements model 143 may also estimate the global QRSonset/offset as shown in FIG. 4 . The global ECG measurements model 143may use the global QRS onset/offsets and the algorithmically derived 12lead set to determine the lead-by-lead ECG measurements. In a standardresting 12-lead analysis, lead-by-lead ECG measurements are important inaddition to the ECG global parameters described herein. Lead-by-leadmeasurements may include the amplitude, duration and the polarity of allmajor ECG morphology components including P wave, QRS complex, ST wave,and T wave. Since morphology determinations are mainly decided by theECG morphology model 142, the final ECG morphology logic 152 may use thelead-by-lead ECG measurements to assist in the final classification,which reduces dependency on having accurate ECG measurements to acertain degree. Indeed, the lead-by-lead ECG measurements can help toprevent some obvious classification errors in case the ECG rhythmclassification model 141, the ECG morphology model 142 and the globalECG measurements model 143 cannot capture them, or produce multipleclassifications from which one needs to be selected. For example, duringmorphology classification, if the ECG morphology model 142 classifiesboth LBBB and Acute MI, the final ECG morphology logic 152 may make afinal classification based on QRS duration, ST segment, and in somecases the T wave. In another example, during ECG rhythm analysis, afalse positive classification of AFIB might be avoided by checking the Pwave measurements including the PR interval and the P wave amplitude.

The final determination stage 150 may include logic to make correctexclusions/inclusions based on cardiology science and clinical practice.The final determination stage 150 may perform exclusion under variousconditions. Since both the ECG rhythm classification model 141 and theECG morphology model 142 are trained for non-exclusive multi-classclassifications, it is possible that multiple abnormalities can beidentified/classified. In some cases, multiple abnormal conditions canbe identified for the same ECG, such as ischemia and infarction, orbundle branch blocks and ischemia. But in other cases, especially duringECG rhythm analysis, certain abnormalities can affect anotherabnormality's detection, or they can be mutually exclusive, like e.g.,sinus rhythm and AFIB, or pace rhythm with sinus, etc. The finaldetermination stage 150 may operate in one of two different modes: ahigh specificity mode and a high sensitivity mode. The high specificitymode may include a higher threshold for model output probability as wellas more conditions based on lead-by-lead measurements. The highsensitivity mode may include a lower threshold for model outputprobability (e.g., 70%). In some embodiments, in high sensitivity mode,if the probability is very high (e.g., greater than 95%) then the modelmay immediately determine that the determined classification is thefinal classification.

As discussed herein, the final determination stage 150 may include finalECG rhythm logic 151 and final ECG morphology logic 152. The final ECGrhythm logic 151 may include logic to find a dominant rhythm from thefollowing list: Paced rhythm, Sinus/Normal Sinus, Atrial fibrillation,Atrial flutter, Wide QRS tachycardia, Junctional rhythm, andUndetermined rhythm. If the dominant rhythm is sinus rhythm, then thefinal ECG rhythm logic 151 may attempt to find the associated sinusarrythmias. The input parameters to the final ECG rhythm logic 151 areas follows: The output of the ECG rhythm classification model 141, theglobal ECG measurements (PR interval, QRS duration, QT interval, andheart rate), and the R-R interval sequence of the dominant beats. Alldeterminations of the final ECG rhythm logic 151 are made in 2 modes:High specificity, and high sensitivity as discussed above.

FIG. 9 illustrates the final ECG rhythm logic 151 as various sets ofconditions for finding the dominant rhythm. The final ECG rhythm logic151 may determine a pace rhythm as the dominant rhythm if the output ofthe ECG rhythm classification model 141 indicates that the probabilityof pace rhythm is high (e.g., greater than 0.8) and that the probabilityof pace rhythm is greater than the probability of any other rhythms. Inaddition, the heart rate must be less than 80 bpm, the QRS duration mustbe less than 150 ms, and the R-R variability must be small. If the pacerhythm is confirmed, the final ECG rhythm logic 151 may not perform anyother checks for other rhythms.

The final ECG rhythm logic 151 may determine (i.e., confirm a sinusrhythm classification from the ECG rhythm classification model 141) asinus rhythm as the dominant rhythm if the output of the ECG rhythmclassification model 141 indicates that the probability of sinus rhythmis high (e.g., greater than 0.7) OR that the probability of a pacerhythm is less than the probability of other rhythms. In addition, thePR interval must be greater than 80 ms OR the probability of sinusrhythm must be is very high (e.g., greater than 0.95). Here, a properrange of PR interval indicate a valid P wave exist, which is the keyfeature for Sinus rhythm. This final analysis is to reinforce the DNNmodel's results. It should be noted that the specificity and sensitivitysettings for sinus rhythms are opposite from other abnormal rhythmssince a high specificity setting for another abnormal rhythm also meansa higher sensitivity setting for the sinus rhythm. To emphasize thishigh specificity, a sinus rhythm is confirmed before other abnormalrhythms, except for the pace rhythm. If the ECG rhythm classificationmodel 141 outputs a very high probability of sinus/normal sinus, theconfirmation is made without checking for other rhythms.

The final ECG rhythm logic 151 may determine a Afib/Aflutter as thedominant rhythm if the output of the ECG rhythm classification model 141indicates that the probability of Afib/aflutter is higher than otherdominant rhythm (e.g., greater than 0.7) and the probability of pacerhythm is smaller than other rhythms. In addition, the PR interval mustbe less than 30 ms and there must be no valid P wave. If the probability(Afib) is less than the probability (aflutter), then the final ECGrhythm logic 151 may determine that aflutter is the dominant rhythm.

If the final ECG rhythm logic 151 rules out all other rhythms as thedominant rhythm, it may check if the junctional rhythm is the dominantrhythm based on whether there is a high probability of junctional rhythmfrom the ECG rhythm classification model 141 and a low R-R variation. Ifboth of these conditions are met, then the final ECG rhythm logic 151may determine that the junctional rhythm is the dominant rhythm.

If the final ECG rhythm logic 151 still has not determined a dominantrhythm after checking the junctional rhythm, it may check if there is asinus rhythm using a more relaxed condition set. More specifically, thefinal ECG rhythm logic 151 may lower the threshold for probability ofsinus rhythm to 0.6 and require that there be a valid P wave and thatall global measurements are in their normal range (e.g., PR [120, 200],QRS duration <115, QTcF<460 ms).

In some embodiments, if the final ECG rhythm logic 151 confirms a sinusrhythm, it may attempt to determine one or more associated arrythmiasare checked. More specifically, if there is a high probability of a 1stdegree Atrial-Ventricular Conduction Block (AVB) and a PR intervalgreater than 220 ms, the final ECG rhythm logic 151 may determine that a1st degree AVB is present. If there is a high probability of a 2nddegree AVB, the probability of the 2nd degree AVB is greater than theprobability of the 3rd degree AVB, and the probability of the 2nd degreeAVB is larger than other Marked arrhythmia probabilities, the final ECGrhythm logic 151 may determine that a 2nd degree AVB is present. Ifthere is a high probability of a 3rd degree AVB, and the probability ofthe 3rd degree AVB is larger than other arrhythmia probabilities, thefinal ECG rhythm logic 151 may determine that a 3rd degree AVB ispresent. If there is a high probability of a Sinus bradycardia, and theheart rate is less than 50 bpm, the final ECG rhythm logic 151 maydetermine that a Sinus bradycardia is present. If there is a highprobability of a Sinus Tachycardia, and the heart rate is greater than100 bpm, the final ECG rhythm logic 151 may determine that a SinusTachycardia is present. If there is a high probability (e.g., greaterthan 0.8) of a marked (severe) sinus arrhythmia, the final ECG rhythmlogic 151 may determine that a marked sinus arrhythmia is present.

The final ECG morphology logic 152 may include logic to reinforce theresults of morphology DNN model and to improve the specificity of majormorphology abnormality determinations. The ECG morphology model 142 alsoworks in a nonexclusive mode, meaning multiple abnormal conditions canbe classified for the same ECG. Although multiple conditions can happenphysiologically, it might be helpful to focus on the main source of anabnormal condition, instead of multiple sources. For example, in acutemyocardial infarction (Acute MI) interpretations, adding ST segmentchanges on top of the DNN model probability is a way to improvespecificity. But if the bundle branch block, especially the left bundlebranch block (LBBB) is confirmed, then the ST segment deflection can besecondary due to depolarization change, which can be difficult todifferentiate from primary repolarization. Therefore, Acute MI finaldetermination is not determined if LBBB or Pace ECG is confirmed.

The final ECG morphology logic 152 may make final morphologydeterminations based on the classifications from one or more of the ECGrhythm classification model 141 and the ECG morphology model 142 infollowing order: Pace ECG, RBBB, LBBB, Other Intra-Ventricular Blocks,LVH, RVH, Old MI, Acute MI, Other ST Elevation, Ischemia, Long QT,Atrial enlargement, Normal ECG. Each condition discussed below isgeneral for both the high sensitivity and the high specificity modes,with only the thresholds being relatively different.

The final ECG morphology logic 152 may determine a pace ECG (i.e.,confirm a pace ECG classification from the ECG morphology model 142) ifboth the ECG rhythm classification model 141 and ECG morphology model142 indicate that the probability of a pace ECG is very high (e.g.,greater than 0.95), and the R-R beat variation is small (RR_std/RR_meanis less than 0.04). If the final ECG morphology logic 152 confirms thepace ECG classification, it may skip performing checks for any othermorphologies.

The final ECG morphology logic 152 may determine a RBBB (i.e., confirm aRBBB classification from the ECG morphology model 142 if the probabilityof RBBB indicated by the ECG morphology model 142 classification is high(e.g., greater than 0.8), and the QRS duration is greater than 120 ms,OR if the probability of RBBB indicated by the ECG morphology model 142classification is greater than 0.95 and the QRS duration is greater than110 ms. If the final ECG morphology logic 152 confirms the RBBBclassification, it may skip performing checks for any othermorphologies.

The final ECG morphology logic 152 may determine a LBBB (i.e., confirm aLBBB classification from the ECG morphology model 142) if theprobability of LBBB indicated by the ECG morphology model 142classification is high (e.g., greater than 0.8), and the QRS duration isgreater than 125 ms OR, if the probability of LBBB indicated by the ECGmorphology model 142 classification is greater than 0.95 and the QRSduration is greater than 115 ms. If the final ECG morphology logic 152confirms the LBBB classification, it may skip performing checks for anyother morphologies.

The final ECG morphology logic 152 may determine any other BBB (i.e.,confirm a classification of any other BBB from the ECG morphology model142) if the probability of such BBB indicated by the ECG morphologymodel 142 classification is high (e.g., greater than 0.8) and the QRSduration is greater than 115 ms OR, the probability of such BBBindicated by the ECG morphology model 142 classification is greater than0.90 and the QRS duration is greater than 110 ms.

The final ECG morphology logic 152 may determine an LVH morphology(i.e., confirm an LVH classification from the ECG morphology model 142)if the probability of LVH indicated by the ECG morphology model 142classification is very high (e.g., greater than 0.99) OR, if theprobability of LVH indicated by the ECG morphology model 142classification is high (e.g., greater than 0.9) and either one of the Vleads has a QRS deflection greater than 2500 uV or the R_aVL is greaterthan 1000 uV (microvolts). It should be noted that LVH determinationsusually require some ECG amplitude measurements. But since only 2precordial leads are used, and the ECG is more aggressively lowpassfiltered, the conventional amplitude criteria for LVH is not directlyapplied. Instead, the ECG morphology model 142 output is used inconjunction with a set of minimum amplitude and axis measurementschecks. The final ECG morphology logic 152 may determine an RVHmorphology (i.e., confirm an RVH classification from the ECG morphologymodel 142) if the probability of LVH indicated by the ECG morphologymodel 142 classification is very high (e.g., greater than 0.9) and theQRS axis is greater than 90 degrees.

The final ECG morphology logic 152 may determine an acute anterior MImorphology (i.e., confirm an acute anterior MI classification from theECG morphology model 142) if the probability of acute anterior MIindicated by the ECG morphology model 142 classification is very high(e.g., greater than 0.95), at least one V lead has an ST greater than200 uV and an ST/T ratio greater than 0.3, and at least one of thereciprocal lead (limb leads) has an ST less than −50 uV (ST depression).Alternatively, the final ECG morphology logic 152 may determine an acuteanterior MI morphology if the probability of acute anterior MI is veryhigh (e.g., greater than 0.99) and at least one V lead has an ST greaterthan 160 uV and an ST/T ratio greater than 0.3.

The final ECG morphology logic 152 may determine an acute inferior MImorphology (i.e., confirm an acute inferior MI classification from theECG morphology model 142) if the probability of acute inferior MIindicated by the ECG morphology model 142 classification is very high(e.g., greater than 0.95), and at least one V lead has an ST greaterthan 100 uV and an ST/T ratio greater than 0.3. Alternatively, the finalECG morphology logic 152 may determine an acute inferior MI morphologyif the probability of acute anterior MI is very high (e.g., greater than0.99) and at least one inferior lead has an ST greater than 60 uV and anST/T ratio greater than 0.3.

The final ECG morphology logic 152 may determine an acute lateral MImorphology (i.e., confirm an acute lateral MI classification from theECG morphology model 142) if the probability of acute lateral MIindicated by the ECG morphology model 142 classification is very high(e.g., greater than 0.95), and at least one lateral lead has an STgreater than 100 uV and an ST/T ratio greater than 0.3. Alternatively,the final ECG morphology logic 152 may determine an acute lateral MImorphology if the probability of acute anterior MI is very high (e.g.,greater than 0.99) and at least one lateral lead has an ST greater than60 uV and an ST/T ratio greater than 0.3.

The final ECG morphology logic 152 may determine a previous myocardialinfarction (old MI) morphology (i.e., confirm an old MI classificationfrom the ECG morphology model 142) if the probability of old MIindicated by the ECG morphology model 142 classification is very high(e.g., greater than 0.90), and there is a significant Q wave.

The final ECG morphology logic 152 may determine an ischemia morphology(i.e., confirm an ischemia classification from the ECG morphology model142) if the probability of ischemia indicated by the ECG morphologymodel 142 classification is very high (e.g., greater than 0.90), andthere is a significant ST depression corresponding to the specificationlocation calls in the lead groups: Anterior leads: ST in V2, V4;Inferior leads: II, III, aVF; lateral leads: I, aVL.

The final ECG morphology logic 152 may determine a prolonged QTmorphology (i.e., confirm a prolonged QT classification from the ECGmorphology model 142) if the probability of prolonged QT indicated bythe ECG morphology model 142 classification is very high (e.g., greaterthan 0.90), and the QTcF (Corrected QT interval by Fridericia formula)is greater than 460 ms. The only exclusion condition for Prolonged QT isPaced ECG.

The final ECG morphology logic 152 may determine a normal morphology(i.e., confirm a normal classification from the ECG morphology model142) if the ECG morphology model 142 did not detect any of the abnormalmorphology conditions and rhythm conditions listed above, sinus rhythmis detected, the heart rate is higher than 50 bpm and lower than 90 bpm,the QRS duration is not longer than 120 ms, the PR interval is smallerthan 230 ms, the QTcF is less than 460 ms, and the P, QRS and T axes areall larger than 0.

FIG. 10 is a flow diagram of a method 1000 for training and implementingan ML model for reduced lead set ECG interpretation, in accordance withsome embodiments of the present disclosure. Method 1000 may be performedby processing logic that may comprise hardware (e.g., circuitry,dedicated logic, programmable logic, a processor, a processing device, acentral processing unit (CPU), a system-on-chip (SoC), etc.), software(e.g., instructions running/executing on a processing device), firmware(e.g., microcode), or a combination thereof. In some embodiments, themethod 1000 may be performed by a computing device (e.g., computingdevice 101 illustrated in FIG. 3 ).

At block 1005, the ECG rhythm classification model 141 may be trainedusing any sufficiently large training data set. For example, thetraining data set may include 1 million labeled ECGs, with another 250Klabeled ECGs for use as an independent test set. In the example of FIG.6A, the batch size is 512, the learning rate is 0.001, and the drop rate0.05. A total of 20 epochs are set. The final model is selected from thesmallest validation error. A training weight matrix is used forbalancing a large difference in prevalence among all determinations. Forthe lower prevalence determinations, a higher weight is used when thetraining error is backpropagated.

Referring also to FIG. 6 , the convolution layers 610 may utilize aresidual block structure that utilizes combinations of convolutionlayers and skip layers. A skip layer (also referred to as a skipconnection or shortcut connections) may enable a residual block in thedeep learning models to address the problem of vanishing and explodinggradients, which can occur when training very deep neural networks. Thisis done by adding the input of a layer directly to the output of one ormore subsequent layers, creating a “shortcut” for the gradient to flowthrough. As a result, different parts of the convolution layers 610 willbe trained at different rates for different training data points basedon how the error flows backward through the layers of the convolutionlayers 610. FIG. 6 illustrates a residual block 610A, where a skip layeris used to skip the training of the convolution layer 2 during learningof an identity function X. ReLu may represent a nonlinear activationfunction.

In addition to the residual blocks, the ECG rhythm classification model141 may include 2 dense layers 615 and 1 classification layer 620. Insome embodiments, the ECG rhythm classification model 141 may furtherinclude a batch normalization layer and dropout layer for every residualblock and dense layer to improve its robustness or generalization onceit is trained (not every batch normalization and dropout layer shown inFIG. 6 ). The ECG rhythm classification model 141 is designed toclassify rhythm determinations, examples of which are shown in table 1(below). The classification layer 620 output may have both positive andnegative probabilities for each determination. In some embodiments, foreach of the 8 lead ECG signals (down-sampled to 150 Hz), 9.5 seconds outof the 10 seconds of data is used for the input and the remaining 0.5seconds of data may be used for augmentation with a time-shiftingtechnique (random data shifting). This random data shifting is one ofthe data augmentation methods used to improve the generalization of thetrained DNN models, which will result in the DNN models being lesssensitive to the start and the end times of the input ECG rhythm data.

At block 1010, the ECG morphology model 142 may be trained with the sametraining data set as the ECG rhythm classification model 141 (which mayinclude median beat data for each training ECG). In the example of FIG.6A, the batch size is 512, the learning rate is 0.001, and the drop rate0.05. A total of 20 epochs are set. The final model is selected from thesmallest validation error. A morphology training weight matrix is alsoused for balancing a large difference in prevalence among alldeterminations. For the lower prevalence determinations, a higher weightis used when the training error is backpropagated. The ECG morphologymodel 142 is designed to classify ECG morphology determinations,examples of which are shown in table 2 (below). The classification layerof the ECG morphology model 142 has both positive and negativeprobabilities for each determination.

Referring also to FIG. 7A, the ECG morphology model 142 may capturemorphology changes for each lead and relative changes from lead to lead,referred to as changes in the spatial domain. The ECG morphology model142 may have a similar model structure as the ECG rhythm classificationmodel 141, but with a smaller lead formation layer and fewer residualblock layers, since the median beat 8 lead signal has a much shorterlength than the 10 second duration of the 8 lead ECG signal input to theECG rhythm classification model 141. In addition, all median beats arerelatively aligned around their QRS onset. Another important designconsideration for the Morphology model QRS region, is that thevariations along the time axis are relatively smaller than rhythmsignals.

To capture the spatial domain changes, the ECG morphology model 142 mayform an ECG imaging by laying out of all the leads in a 2-D matrix/imagepattern with the X-axis as time and the Y-axis as a lead order of [aVL,I, aVR, II, aVF, III, v2, v4], where the limb leads are reordered in aso-called Cabrera sequence. The ECG morphology model 142 may process the2-D ECG signal matrix with a 2-D convolutional filter like in imageclassification cases. This method is more effective than applyingmultiple 1-D convolutional filters to each lead separately, which hasbeen used by most other ECG training models. Since ECG is a projectionof heart's electrical depolarization and repolarization process to thetorso, a spatial 2-D signal matrix can capture this projection moreeffectively. Therefore, 2-D convolutional modes are applied to this ECGimage input.

At block 1015, the global ECG measurements model 143 may be trained. Theglobal ECG measurements model 143 measure ECG global parametersincluding PR interval, QRS duration, QT interval, and heart rate. Theseparameters are referred to as ECG global measurements because they arenot lead-specific and are often estimated based on a combination ofmultiple leads. These parameters are often detected with complicatedsignal processing and feature detection algorithms based on domainknowledge. However, embodiments of the present disclosure estimate theseECG parameters using a deep learning model (the global ECG measurementsmodel 143) applied to the median beat 8 lead signal generated from the 8lead ECG signal. Because the ECG global parameters are closely relatedto ECG morphology, the global ECG measurements model 143 can be trainedto perform a regression of the median beat 8 lead ECG signal to detectthese parameters. Another aim of training the global ECG measurementsmodel 143 is to also be able to identify the absence of a valid P waveas in atrial fibrillation cases.

FIG. 8 illustrates a detailed structure of the global ECG measurementsmodel 143. The structure of the global ECG measurements model 143 islike the structure of the ECG morphology model 142 except the finalclassification layer of the global ECG measurements model 143 isreplaced by a regression layer 820. In addition, the global ECGmeasurements model 143 does not have the batch normalization layers ofthe ECG morphology model 142 since they are more useful forclassification than for regression. The global ECG measurements model143 may receive as input a median beat 4 lead signal based on the leadset [I, II, v2, v4]. More specifically, the median beats generationmodule 133 may determine the median beats for each lead of the lead set[I, II, v2, v4] as discussed hereinabove and provide this median beat 4lead signal to the global ECG measurements model 143. The global ECGmeasurements model 143 is trained to output three intervals: PRinterval, QRS duration, and QT interval by minimizing the absolute errorbetween labeled (physician-estimated) intervals from the training dataand the model-estimated intervals.

At block 1020, ECG rhythm and ECG morphology classifications may bedetermined for an input ECG by the ECG rhythm classification model 141and the ECG morphology model 142 respectively. At block 1025, the globalparameters for the input ECG may be determined by the global ECGmeasurements model 143.

The final determination stage 150 may include final ECG rhythm logic 151and final ECG morphology logic 152. At block 1030, the final ECG rhythmlogic 151 may determine a dominant rhythm from the following list: Pacedrhythm, Sinus/Normal Sinus, Atrial fibrillation, Atrial flutter, WideQRS tachycardia, Junctional rhythm, and Undetermined rhythm. If thedominant rhythm is sinus rhythm, then the final ECG rhythm logic 151 mayattempt to find the associated sinus arrythmias. The input parameters tothe final ECG rhythm logic 151 are as follows: The output of the ECGrhythm classification model 141, the global ECG measurements (PRinterval, QRS duration, QT interval, and heart rate), and the R-Rinterval sequence of the dominant beats. All determinations of the finalECG rhythm logic 151 are made in 2 modes: High specificity, and highsensitivity as discussed above.

The final ECG morphology logic 152 may make final morphologydeterminations based on the classifications from one or more of the ECGrhythm classification model 141 and the ECG morphology model 142 infollowing order: Pace ECG, RBBB, LBBB, Other Intra-Ventricular Blocks,LVH, RVH, Old MI, Acute MI, Other ST Elevation, Ischemia, Long QT,Atrial enlargement, Normal ECG. Each condition discussed below isgeneral for both the high sensitivity and the high specificity modes,with only the thresholds being relatively different. The inputparameters to the final ECG morphology logic 152 are as follows: theoutput of the ECG morphology model 142, the output of the ECG rhythmclassification model 141 (in some cases), and the global ECGmeasurements (PR interval, QRS duration, QT interval, and heart rate).

FIG. 11 illustrates a diagrammatic representation of a machine in theexample form of a computer system 1100 within which a set ofinstructions, for causing the machine to perform any one or more of themethodologies discussed herein for performing reduced lead ECGinterpretation.

In alternative embodiments, the machine may be connected (e.g.,networked) to other machines in a local area network (LAN), an intranet,an extranet, or the Internet. The machine may operate in the capacity ofa server or a client machine in a client-server network environment, oras a peer machine in a peer-to-peer (or distributed) networkenvironment. The machine may be a personal computer (PC), a tablet PC, aset-top box (STB), a Personal Digital Assistant (PDA), a cellulartelephone, a web appliance, a server, a network router, a switch orbridge, a hub, an access point, a network access control device, or anymachine capable of executing a set of instructions (sequential orotherwise) that specify actions to be taken by that machine. Further,while only a single machine is illustrated, the term “machine” shallalso be taken to include any collection of machines that individually orjointly execute a set (or multiple sets) of instructions to perform anyone or more of the methodologies discussed herein. In one embodiment,computer system 1100 may be representative of a server.

The exemplary computer system 1100 includes a processing device 1102, amain memory 1104 (e.g., read-only memory (ROM), flash memory, dynamicrandom access memory (DRAM), a static memory 1106 (e.g., flash memory,static random access memory (SRAM), etc.), and a data storage device1118, which communicate with each other via a bus 1130. Any of thesignals provided over various buses described herein may be timemultiplexed with other signals and provided over one or more commonbuses. Additionally, the interconnection between circuit components orblocks may be shown as buses or as single signal lines. Each of thebuses may alternatively be one or more single signal lines and each ofthe single signal lines may alternatively be buses.

Computing device 1100 may further include a network interface device1108 which may communicate with a network 1120. The computing device1100 also may include a video display unit 1110 (e.g., a liquid crystaldisplay (LCD) or a cathode ray tube (CRT)), an alphanumeric input device1112 (e.g., a keyboard), a cursor control device 1114 (e.g., a mouse)and an acoustic signal generation device 1116 (e.g., a speaker). In oneembodiment, video display unit 1110, alphanumeric input device 1112, andcursor control device 1114 may be combined into a single component ordevice (e.g., an LCD touch screen).

Processing device 1102 represents one or more general-purpose processingdevices such as a microprocessor, central processing unit, or the like.More particularly, the processing device may be complex instruction setcomputing (CISC) microprocessor, reduced instruction set computer (RISC)microprocessor, very long instruction word (VLIW) microprocessor, orprocessor implementing other instruction sets, or processorsimplementing a combination of instruction sets. Processing device 1102may also be one or more special-purpose processing devices such as anapplication specific integrated circuit (ASIC), a field programmablegate array (FPGA), a digital signal processor (DSP), network processor,or the like. The processing device 1102 is configured to executeinstructions 1125, for performing the operations and steps discussedherein.

The data storage device 1115 may include a machine-readable storagemedium 628, on which is stored one or more sets of instructions 1125(e.g., software) embodying any one or more of the methodologies offunctions described herein. The instructions 1125 may also reside,completely or at least partially, within the main memory 1104 or withinthe processing device 1102 during execution thereof by the computersystem 1100; the main memory 1104 and the processing device 1102 alsoconstituting machine-readable storage media. The instructions 1125 mayfurther be transmitted or received over a network 1120 via the networkinterface device 1108.

While the machine-readable storage medium 1128 is shown in an exemplaryembodiment to be a single medium, the term “machine-readable storagemedium” should be taken to include a single medium or multiple media(e.g., a centralized or distributed database, or associated caches andservers) that store the one or more sets of instructions. Amachine-readable medium includes any mechanism for storing informationin a form (e.g., software, processing application) readable by a machine(e.g., a computer). The machine-readable medium may include, but is notlimited to, magnetic storage medium (e.g., floppy diskette); opticalstorage medium (e.g., CD-ROM); magneto-optical storage medium; read-onlymemory (ROM); random-access memory (RAM); erasable programmable memory(e.g., EPROM and EEPROM); flash memory; or another type of mediumsuitable for storing electronic instructions.

The preceding description sets forth numerous specific details such asexamples of specific systems, components, methods, and so forth, inorder to provide a good understanding of several embodiments of thepresent disclosure. It will be apparent to one skilled in the art,however, that at least some embodiments of the present disclosure may bepracticed without these specific details. In other instances, well-knowncomponents or methods are not described in detail or are presented insimple block diagram format in order to avoid unnecessarily obscuringthe present disclosure. Thus, the specific details set forth are merelyexemplary. Particular embodiments may vary from these exemplary detailsand still be contemplated to be within the scope of the presentdisclosure.

Additionally, some embodiments may be practiced in distributed computingenvironments where the machine-readable medium is stored on and orexecuted by more than one computer system. In addition, the informationtransferred between computer systems may either be pulled or pushedacross the communication medium connecting the computer systems.

Embodiments of the claimed subject matter include, but are not limitedto, various operations described herein. These operations may beperformed by hardware components, software, firmware, or a combinationthereof.

Although the operations of the methods herein are shown and described ina particular order, the order of the operations of each method may bealtered so that certain operations may be performed in an inverse orderor so that certain operation may be performed, at least in part,concurrently with other operations. In another embodiment, instructionsor sub-operations of distinct operations may be in an intermittent oralternating manner.

The above description of illustrated implementations of the invention,including what is described in the Abstract, is not intended to beexhaustive or to limit the invention to the precise forms disclosed.While specific implementations of, and examples for, the invention aredescribed herein for illustrative purposes, various equivalentmodifications are possible within the scope of the invention, as thoseskilled in the relevant art will recognize. The words “example” or“exemplary” are used herein to mean serving as an example, instance, orillustration. Any aspect or design described herein as “example” or“exemplary” is not necessarily to be construed as preferred oradvantageous over other aspects or designs. Rather, use of the words“example” or “exemplary” is intended to present concepts in a concretefashion. As used in this application, the term “or” is intended to meanan inclusive “or” rather than an exclusive “or”. That is, unlessspecified otherwise, or clear from context, “X includes A or B” isintended to mean any of the natural inclusive permutations. That is, ifX includes A; X includes B; or X includes both A and B, then “X includesA or B” is satisfied under any of the foregoing instances. In addition,the articles “a” and “an” as used in this application and the appendedclaims should generally be construed to mean “one or more” unlessspecified otherwise or clear from context to be directed to a singularform. Moreover, use of the term “an embodiment” or “one embodiment” or“an implementation” or “one implementation” throughout is not intendedto mean the same embodiment or implementation unless described as such.Furthermore, the terms “first,” “second,” “third,” “fourth,” etc. asused herein are meant as labels to distinguish among different elementsand may not necessarily have an ordinal meaning according to theirnumerical designation.

It will be appreciated that variants of the above-disclosed and otherfeatures and functions, or alternatives thereof, may be combined intomay other different systems or applications. Various presentlyunforeseen or unanticipated alternatives, modifications, variations, orimprovements therein may be subsequently made by those skilled in theart which are also intended to be encompassed by the following claims.The claims may encompass embodiments in hardware, software, or acombination thereof.

What is claimed is:
 1. A method comprising: training a first deep neuralnetwork (DNN) to classify electrocardiogram (ECG) rhythmcharacteristics, wherein a training weight matrix is used to balance adifference in prevalence among classifications during training; traininga second DNN to classify ECG morphology characteristics; training athird DNN to identify global ECG parameters, wherein both the first andsecond DNNs comprise a classification layer and the third DNN comprisesa regression layer; and implementing the first, second, and third DNNsas an ensemble DNN for ECG interpretation.
 2. The method of claim 1,further comprising: in response to receiving an ECG: classifying rhythmcharacteristics of the ECG using the first DNN; classifying morphologycharacteristics of the ECG using the second DNN; and identifying globalparameters of the ECG using the third DNN; and applying a set ofdecision logic to the ECG rhythm characteristics and the ECG morphologycharacteristics to diagnose one or more conditions indicated by the ECGbased at least in part on the ECG global parameters.
 3. The method ofclaim 1, wherein: the classification of the rhythm characteristics ofthe ECG comprises a probability for each of a set of rhythms; and theclassification of the morphology characteristics of the ECG comprises aprobability for each of a set of morphologies.
 4. The method of claim 2,wherein the set of decision logic comprises: a first set of criteria forvalidating the classification of the rhythm characteristics of the ECGbased at least in part on the global parameters of the ECG; and a secondset of criteria for validating the classification of the morphologycharacteristics of the ECG based at least in part on the globalparameters and the validated classification of the rhythmcharacteristics of the ECG.
 5. The method of claim 3, wherein one ormore of the first, second and third DNN comprise convolution neuralnetworks (CNNs).
 6. The method of claim 1, wherein each of the first,second and third DNNs comprises a convolution block having a residualblock structure that combines a set of convolution layers and a set ofskip layers.
 7. The method of claim 5, further comprising: using arandom data shifting technique to further generalize each of the first,second and third DNNs.
 8. A system comprising: an electrocardiogram(ECG) monitor configured to perform an ECG of a person and transmit theECG; and a computing device configured to: in response to receiving theECG from the ECG monitor: classify rhythm characteristics of the ECGusing a first deep neural network (DNN); classify morphologycharacteristics of the ECG using a second DNN; and identify globalparameters of the ECG using a third DNN; and apply a set of decisionlogic to the ECG rhythm characteristics and the ECG morphologycharacteristics to diagnose one or more conditions indicated by the ECGbased at least in part on the ECG global parameters.
 9. The system ofclaim 8, wherein the processing device is further to: train the firstdeep neural network (DNN) to classify electrocardiogram (ECG) rhythmcharacteristics, wherein a training weight matrix is used to balance adifference in prevalence among classifications during training; trainingthe second DNN to classify ECG morphology characteristics; training thethird DNN to identify global ECG parameters, wherein both the first andsecond DNNs comprise a classification layer and the third DNN comprisesa regression layer
 10. The system of claim 8, wherein: theclassification of the rhythm characteristics of the ECG comprises aprobability for each of a set of rhythms; and the classification of themorphology characteristics of the ECG comprises a probability for eachof a set of morphologies.
 11. The system of claim 8, wherein the set ofdecision logic comprises: a first set of criteria for validating theclassification of the rhythm characteristics of the ECG based at leastin part on the global parameters of the ECG; and a second set ofcriteria for validating the classification of the morphologycharacteristics of the ECG based at least in part on the globalparameters and the validated classification of the rhythmcharacteristics of the ECG.
 12. The system of claim 10, wherein one ormore of the first, second and third DNN comprise convolution neuralnetworks (CNNs).
 13. The system of claim 8, wherein each of the first,second and third DNNs comprises a convolution block having a residualblock structure that combines a set of convolution layers and a set ofskip layers.
 14. The system of claim 12, wherein the processing deviceis further to: use a random data shifting technique to furthergeneralize each of the first, second and third DNNs.
 15. Anon-transitory computer-readable medium having instructions storedthereon which, when executed by a processing device, cause theprocessing device to: train a first deep neural network (DNN) toclassify electrocardiogram (ECG) rhythm characteristics; train a secondDNN to classify ECG morphology characteristics; train a third DNN toidentify global ECG parameters; in response to receiving an ECG:classify rhythm characteristics of the ECG using the first DNN; classifymorphology characteristics of the ECG using the second DNN; and identifyglobal parameters of the ECG using the third DNN; and apply a set ofdecision logic to the ECG rhythm characteristics and the ECG morphologycharacteristics to diagnose one or more conditions indicated by the ECGbased at least in part on the ECG global parameters.
 16. Thenon-transitory computer-readable medium of claim 15, wherein: theclassification of the rhythm characteristics of the ECG comprises aprobability for each of a set of rhythms; and the classification of themorphology characteristics of the ECG comprises a probability for eachof a set of morphologies.
 17. The non-transitory computer-readablemedium of claim 15, wherein the set of decision logic comprises: a firstset of criteria for validating the classification of the rhythmcharacteristics of the ECG based at least in part on the globalparameters of the ECG; and a second set of criteria for validating theclassification of the morphology characteristics of the ECG based atleast in part on the global parameters and the validated classificationof the rhythm characteristics of the ECG.
 18. The non-transitorycomputer-readable medium of claim 17, wherein one or more of the first,second and third DNN comprise convolution neural networks (CNNs). 19.The non-transitory computer-readable medium of claim 15, wherein each ofthe first, second and third DNNs comprises a convolution block having aresidual block structure that combines a set of convolution layers and aset of skip layers.
 20. The non-transitory computer-readable medium ofclaim 17, wherein the processing device is further to: use a random datashifting technique to further generalize each of the first, second andthird DNNs.